Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/52103
標題: 利用近紅外光譜技術進行台灣烏龍茶快速分析與鑑別之研究
Studies on the Rapid Analysis and Classification of Taiwan Oolong Tea by Near Infrared Spectroscopy
作者: 劉士綸
Liu, Shih-Lun
關鍵字: 近紅外光譜技術;Near Infrared Spectroscopy (NIR);台灣烏龍茶;快速分析;鑑別;物化成分;Taiwan Oolong Tea;Rapid Analysis;Classification;physicochemical characteristics
出版社: 食品暨應用生物科技學系所
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摘要: 
茶葉是台灣重要的經濟作物,因不同的產地、品種、季節及製造方式等因子,而造就台灣豐富多變的特色茶種類,烏龍茶又是其中最受歡迎的一種。本研究主要之目的在於比較台灣烏龍茶物化特性上之差異,並結合分析快速之近紅外光譜技術 (Near Infrared Spectroscopy, NIRS),嘗試以NIRS進行台灣烏龍茶品質之快速分析與鑑別。本研究之第一部分主要在於探討以物化特性進行台灣烏龍茶之產地、品種、產季與等級之鑑別能力;第二部分在於探討以茶葉之近紅外光譜直接進行台灣烏龍茶之產地、品種、產季與等級之鑑別能力,並與第一部份之結果相比較;第三部分在於探討以NIRS取代台灣烏龍茶傳統物化分析之可行性;第四部分在於結合物化特性與近紅外光譜技術,以台灣三大半球型烏龍茶為樣品,進行種類與等級之比較與鑑別。
第一部分蒐集6個不同產地、6種品種與2個產季之308件台灣烏龍茶茶樣,分析其水分、pH值、總兒茶素含量、總多元酚含量、總氮量、總游離胺基酸含量、咖啡因與個別兒茶素中的GC、EGC、C、EC、EGCG及ECG等主要物化成分。利用主要物化成分含量,以線性鑑別分析法 (linear discriminant analysis, LDA) 進行台灣烏龍茶產地、品種、產季與等級之鑑別。主成分分析 (principal component analysis, PCA) 之結果,累積前三個主成分可解釋68 %之變異,以區分不同產季之效果較佳,而不同產地與品種茶樣之區分僅在同一產季之茶樣中,可約略分辨出來。以16項主要物化成分含量進行線性鑑別部分,對不同產地、品種及產季之茶樣之鑑別成功率分別為85.1 %、76.29 %及100 %。由於台灣烏龍茶間化學組成之變異過大,僅利用16項主要物化成分含量進行產地、品種及產季之鑑別仍有不足,未來納入更多變數後應可提升鑑別效果。以主要物化成分對木柵、桃竹苗、名間、鹿谷、嘉義及台東不同等級之比賽茶樣,其鑑別成功率分別為94.19 %、100 %、92.28 %、96.49 %、64.15 %及66.18 %,以單一產季比賽茶為主所建立之鑑別模式鑑別成功率皆較高。
第二部份同樣利用第一部分之茶樣作為試驗材料,主要目的在利用近紅外線光譜技術進行台灣烏龍茶產地、品種、產季與等級之快速鑑別。結果顯示利用近紅外線光譜配合主成分分析累積前三個主成分可解釋茶樣95 %之變異,其中區分不同產地之茶樣效果最佳,不同品種茶樣之效果次佳。利用近紅外線光譜搭配部分最小平方迴歸分析 (partial least square, PLS) 所得之鑑別模式,鑑別不同產地與不同品種的成功率分別為97.37 % (296 of 304) 與98.36 % (299 of 305)。另利用所得之鑑別模式更可以100 %正確鑑別不同產季之茶樣。以此鑑別模式進一步鑑別不同產地、不同品種與不同產季的茶樣,成功率分別為96.30 %、94.10 %與99.23 %,鑑別成功率與原本模式相當。由此可見利用近紅外線光譜所製得之鑑別模式,能有效鑑別不同產地、品種與產季之台灣台灣烏龍茶,且優於物化特性鑑別之效果。在以NIRS進行茶葉等級之鑑別部分,對木柵、桃竹苗、名間、鹿谷、嘉義及台東不同等級之比賽茶樣,其鑑別成功率僅分別為73.53 %、65.38 %、87.03 %、81.25 %、63.33 %及68.54 %,鑑別效果不及以物化特性進行鑑別。
第三部份在於探討利用近紅外光譜技術替代傳統物理化學分析法快速測定台灣烏龍茶主要成分之可行性。共蒐集348件不同產季之茶樣用於NIRS檢量線之試製及預測,包括產自桃園龍潭、名間、鹿谷、嘉義、台東等地之274件各式烏龍茶、冬季產自木柵之34件鐵觀音茶 (為第一及第二部分所使用之308件茶樣),再加上經不同烘焙溫度與時間處理之40件霧社茶區烏龍茶。16項茶葉之主要成分中可選出5項成分之檢量線具良好之解釋與預測能力,分別為水分、pH值、總兒茶素、總多元酚與總氮含量。其檢量線之解釋能力指標-決定係數 (coefficeint of determination, R2) 分別為0.92、0.87、0.91、0.91及0.93,而預測值與實測值之相關係數 (sample correlation coefficient, r) 也分別為0.90、0.91、0.96、0.95及0.84。利用10件未知樣品進行檢量線預測效果之評估,顯示預測值與實測值間並無顯著性差異,表示可利用此5條檢量線,進行台灣烏龍茶中水分、pH值、總兒茶素、總多元酚與總氮含量之快速檢測。
第四部分是以高山茶 (60件)、凍頂烏龍茶 (46件) 和鐵觀音 (34件) 之比賽茶共140件台灣最重要且製程相近之三大半球型烏龍茶類之19項主要物化成分與近紅外光譜,進行此三種茶類之比較與鑑別。在物化成分部分,分別以逐步判別分析法和主成分分析法結合線性鑑別分析法,探討此三茶類之鑑別。結果顯示,在19項分析的物化成分中,三種茶類有高達10項成分達顯著水準差異 (p<0.05),分別為C、EGCG、總酯型兒茶素、總氮量、總游離胺基酸、茶湯測色值Hunter L、Hunter a、Hunter b、茶湯彩度 (chroma) 和pH值等。整體而言,鐵觀音具有較低pH值、總氮量、總游離胺基酸和高咖啡因、高酯型兒茶素之特徵;高山茶以含游離胺基酸及茶湯pH值較高為主要特徵;凍頂烏龍茶則介於兩者之間,但總游離兒茶素含量與高山茶相近,皆比鐵觀音高。以逐步判別分析法利用物化成分鑑別三大半球型烏龍茶,結果選出7項最適鑑別之物化成分,包括pH值、總游離胺基酸、EGC、咖啡因、ECG、總兒茶素及總酯型兒茶素等,其鑑別成功率為91.43 %。以主成分分析法結合線性鑑別分析法,利用前4項主成分則可達100 % 鑑別成功率。在近紅外光譜進行鑑別之部分,則可100 %鑑別三大茶類。另台灣烏龍茶之發酵程度分析結果則顯示,目前文獻各種烏龍茶之發酵程度有待釐清,高山茶總兒茶素含量不減反增,凍頂烏龍茶與鐵觀音之發酵程度與文獻所載亦有明顯差異。
綜合以上之結果顯示,利用近紅外光譜技術已可取代部分之傳統物化成分之分析工作,且應用於台灣烏龍茶產地、品種與產季之鑑別上,效果明顯優於一般物化成分。

Tea is an important cash crop in Taiwan. The various kinds of tea are produced according to different production areas, varieties, production seasons, and ways of tea making. Oolong tea is the most popular kind of tea in Taiwan. The purposes of this study were to investigate the physicochemical characteristics of Taiwan Oolong tea and the feasibility of using Near Infrared Spectroscopy (NIRS) for rapid analysis and discrimination of Taiwan Oolong tea. The first part of this study investigated the feasibility of discriminating the different varieties, production areas, seasons and grades of Taiwan Oolong tea by physicochemical characteristics. The second part investigated the feasibility of discriminating the different varieties, production areas, seasons and grades of Taiwan Oolong tea by using NIRS spectra directly, and compared with the result of first part. The third part evaluated the feasibility of using NIRS to replace the traditional physicochemical analysis for the major constituents of Taiwan Oolong tea.The fourth part compared the three major semi-sphere types of Taiwan Oolong tea and investigated their discrimination including grades by physicochemical characteristics and NIR spectra.
A total of 308 Taiwan Oolong tea samples with 6 different production areas, 6 tea varieties and 2 different production seasons were collected as materials in the first part. The physicochemical characteristics including moisture, pH, total catechins, total polyphenols, total nitrogen, caffeine, the individual catechins (GC、EGC、C、EC、EGCG、ECG) were analyzed. The linear discriminant analysis (LDA) was used to discriminate the different varieties, production areas, seasons and grades of Taiwan Oolong tea with these physicochemical characteristics.
The principal component analysis (PCA) result showed that the first three principal components could explain the sample variation up to 68 % and classifying the tea samples between different production seasons was better than the other two. The different production areas and varieties of tea samples within the same production seasons were slightly classified.
The discriminant model established by LDA could recognize and identify the tea samples with different production areas, varieties and production seasons to 85.1 %、76.29 % and 100 %, respectively. However, due to the variations among the chemical constituents of Taiwan Oolong tea samples were very large, more variables used to establish the model are needed to improve the discrimination abilities. The physicochemical characteristics were used to discriminate the grades of tea samples from the contests in Mu-Jha, Tao-Chu-Miao, Min-Jian, Lu-Ku, Jia-Yi and Tai-Dong. The discriminant model could recognize and identify the grades of tea samples up to 94.19 %, 100 %, 92.28 %, 96.49 %, 64.15 % and 66.18 %, respectively. The discrimination abilities of discriminant model established by the tea samples from single production season were better than those from 2 production seasons.
The purpose of the second part was to investigate the feasibility of using near infrared spectra from NIRS to rapidly discriminate the different varieties, production areas, seasons and grades of Taiwan Oolong tea. A total of 308 Taiwan Oolong tea samples with 6 different production areas, 6 tea varieties and 2 different production seasons were collected as materials.
The PCA result of NIRS spectra data showed that the first three principal components could explain the sample variation up to 95 %. The ability of classifying different production areas of tea samples by PCA was the best followed by tea varieties. The discriminant model further established by NIRS data with partial least square (PLS) could recognize and identify the production areas, varieties and production seasons of tea samples up to 97.37 % (296 of 304), 98.36 % (299 of 305) and 100 %, respectively. Using the established discriminant model to discriminate the tea samples with different production areas, varieties and production seasons, the prediction results which were correctly identified 94.10 %, 96.30 % and 99.23 %, respectively were close enough to those of the discriminant model. It indicated that the discriminant model established by NIRS spectra data could discriminate the Taiwan Oolong tea among different production areas, varieties and production seasons. The discrimination abilities with NIRS were better than those with physicochemical characteristics. The NIR spectra were used to discriminate the grades of tea samples from the contests in Mu-Jha, Tao-Chu-Miao, Min-Jian, Lu-Ku, Jia-Yi and Tai-Dong. The discriminant model could recognize and identify the grades of tea samples to 73.53 %, 65.38 %, 87.03 %, 81.25 %, 63.33 % and 68.54 %, respectively. The discrimination abilities of discriminant model established by NIR spectra were worse than physicochemical characteristics.
The purpose of the third part was to evaluate the feasibility of using NIRS to replace the traditional physicochemical analysis for the major constituents of Taiwan Oolong tea. Totally 348 tea samples were collected as materials including 274 oolong tea samples produced from Long-Tan (Tao-Yuan), Min-Jian, Lu-Ku, Jia-Yi, Tai-Dong, 34 Tieh-Kuan-Yin produced from Mu-Jha on winter, and 40 oolong tea samples produced from Wu-she with different kinds of electric-roasting treatments. From the NIRS analysis results, it showed that the established calibration curves of 5 out of 16 major constituents had better explanation and prediction abilities than others. The coefficeint of determination (R2) of these 5 calibration curves for moisture, pH, total catechin contents, total polyphenol contents and total nitrogen contents were 0.92, 0.87, 0.91, 0.91 and 0.93, respectively. The sample correlation coefficients (r) for prediction of these 5 constituents were 0.90, 0.91, 0.96, 0.95 and 0.84, respectively. Using ten unknown tea samples to evaluate these 5 calibration curves, there was no significant difference between experimental and NIR predicted data. It indicated that it was feasible to use NIRS to replace the traditional methods for rapidly analyzing the pH and the contents of moisture, total catechins, total polyphenols and total nitrogen in Taiwan Oolong tea.
Total 140 semi-sphere-type Taiwan Oolong tea samples including High-mountain tea (60), Dong-ding Oolong tea (46) and Tiehkuanyin (34) collected from famous tea contests used as materials, the purpose of the fourth part were to compare the major three kinds of tea in Taiwan with physicochemical characteristics and NIR spectra. As the physicochemical characteristics, the discrimination model was established by the stepwise discriminant analysis and PCA with LDA.
The results show that ten out of 19 constituents were significantly different in these three types of tea samples (p<0.05). They were C, EGCG, total ester-type catechins, total nitrogen, Hunter L, a, and b values, chroma and pH. The characteristics of Tiehkuanyin tea samples were low in pH, total nitrogen and total free-type catechins but high in caffeine and ester-type catechins while that of High-mountain tea samples was rich in free-type catechins and pH of tea infusions. The Dong-ding Oolong tea samples were in between these two types of tea samples but the content of total free amino acids was more close to that of High-mountain tea and both higher than that of Tiehkuanyin.
Using results of physicochemical analysis to conduct the stepwise discriminant analysis directly, the results showed that 7 items including pH, total free amino acids, EGC, caffeine, ECG, total catechins and total ester-type catechins, were the most suitable for discrimination. The percentage of successful discrimination was 91.43 %. Using PCA with LDA, the first four components used could discriminate 100 % correctly. The discriminant model established with NIR spectra also could discriminate 100 % of the three major type of tea.
In term of the fermentation level for Taiwan Oolong tea, results indicated that there is a need to clear out the published data in the literature. This study showed that not only the catechins contents in High-mountain tea samples were increased not decreased but also the fermentation levels for other two were also quite different from the data in literatures.
In conclusion, it may say that the NIRS could replace the some traditional methods for rapidly analyzing the Taiwan Oolong tea. The discriminant model established by NIRS could discriminate the production areas, varieties and production seasons of Taiwan Oolong tea instead of analyzing their physicochemical characteristics.
URI: http://hdl.handle.net/11455/52103
Appears in Collections:食品暨應用生物科技學系

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